You will push one button after another , taking the answer from each to feed the next . To see video real-life examples of this process in action go to www . BigSocialMobile . com / StrictlyMarketing .
These are the steps :
Datafy your Perfect Customer : Big data is big for two reason . First , because it is generated during every interaction and its expansion is dynamic . Second , it consists of different types of data that tell us all about a person . 5 types to be exact : explicit , implicit , derived , social and behavioral . We could dedicate a whole column to just these five and how to use them , but for now a quick thumbnail sketch .
Explicit ( what someone tells you : i . e . their name on a web form ); implicit ( what you can infer : what product they are interested in ); derived ( what you can calculate from the given data : such as their age from their DOB or their propensity to buy by crossing implicit with behavioral data ); social ( what social platforms tell you : who is in their social circle ); and behavioral ( what they actually do : what sites / pages they visit , how long they stay , where they click / hover , or on smart terminals their actual eye movement ). These 5 types exist for every physical interaction as well , it ’ s just that we can ’ t datafy them as easily in the physical world ( although big social mobile enterprises do ).
So the first step is to define your perfect customer . This won ’ t be a one-time event . Continuously refinement using more complex variables will give you better performance over time . Start with their demographic information . It is perhaps the least meaningful type of data ( because it relies on stereotypes ) but it is what your organization will relate to first : age , sex , race , culture , geography , or income level , perhaps even height and weight depending on what drives your sales . To this you add every variable from the five types of data that you think influenced this customer being perfect .
To get yourself to think differently it can be helpful to take a group of relatively top performing customers and see what commonalities they share . Look for unexpected or insignificant variable . Datafying your perfect customer isn ’ t a science ; it is an art form .
Like-Kind Analysis : most marketers are familiar with a like-kind analysis . It quite literally takes input variables and finds other people that share them . It is the analysis social platforms use for advertising programs ( when you select keywords or demographics you are setting up the input variables ). Most companies use this same approach , never taking the time to datafy their perfect customer , and therefore the key-words or demographics they chose are commonly used by every company in their industry ( this is what fuels the ‘ ad-word industry ’, making more common words more expensive to sponsor ). As a result most companies experience similar results .
Smarter companies compete on these common words , but place greater emphasis on unexpected commonalities they uncovered while analyzing their perfect customers . This finer granularity enables them to identify smaller pockets of prospects that can then be individually assessed and targeted further with specific content further along in the process . ( In Big Social Mobile I explain in a case study how SLR camera manufacturers are competing with smartphones by using ‘ vacation ’ as a key variable .)
Pattern Analysis : perfect customers didn ’ t become perfect on their own . Your company ’ s marketing and sales process ( both physical and digital aspects of it ) made them perfect . More specifically , the path that your perfect customer followed is the same path that will most effectively create new ones — as quickly as possible , as often as possible . You need only start them on this journey and prod them along when they slow down or get distracted .
5 Strictly Marketing Magazine September / October 2014